3D Reconstruction Based on Grouping Similar Structures for Images Acquired in the Fukushima Daiichi Nuclear Power Station

This paper describes a 3D reconstruction based on grouping similar structures for the aim of generating 3D information for understanding the workspace from images acquired inside the Primary Containment Vessel (PCV) of the Fukushima Daiichi Nuclear Power Station. In the decommissioning works, preliminary surveys are carried out in the PCV, and the workers need to understand the workspace from a large number of camera images, which requires a great deal of effort. We are currently working on 3D reconstruction from PCV camera images; however, one of the challenges is to improve the visibility of the reconstructed model containing noise and artifacts. In this study, we propose a method of grouping similar structures on images and utilizing predicted group labels for 3D reconstruction process to highlight structures shapes and to refine 3D modeling. Our key idea is to perform unsupervised segmentation for grouping similar structures that are suitable for images acquired in the PCV because they are difficult to assign correct semantics for unclear structures and the few learning resources. We show on the reasonable performance the proposed method by validating it using video images of a typical plant environment and survey videos of the PCV taken under adverse conditions, such as radiation noise.